U.S. patent number 10,832,268 [Application Number 15/409,806] was granted by the patent office on 2020-11-10 for modeling customer demand and updating pricing using customer behavior data.
This patent grant is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The grantee listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Norbert M. Binkiewicz, Junlei Chen, Elizabeth J. Chester, Prafulla N. Dawadi, Robert K. Parkin, Emrah Zarifoglu.
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United States Patent |
10,832,268 |
Binkiewicz , et al. |
November 10, 2020 |
Modeling customer demand and updating pricing using customer
behavior data
Abstract
In an aspect of the invention, a computer-implemented method
includes: receiving, by a computing device via computer network, a
plurality of session data records indicating computer network
browsing activity between a plurality of client devices and a
merchant server hosting an online store; aggregating, by the
computing device, a subset of the plurality of session data records
for a single product, of a plurality of products, identified in the
session data records and offered for purchase by the online store;
extracting, by the computing device, features from the aggregated
subset of session data records relating to customer demand for a
the single product; modeling, by the computing device, customer
demand for the single product based on the extracted features;
optimizing, by the computing device, a price for the single product
based on results of the modeling; and publishing, by the computing
device, the optimized price.
Inventors: |
Binkiewicz; Norbert M. (San
Mateo, CA), Chen; Junlei (Shanghai, CN), Chester;
Elizabeth J. (San Francisco, CA), Dawadi; Prafulla N.
(Foster City, CA), Parkin; Robert K. (San Francisco, CA),
Zarifoglu; Emrah (Foster City, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION (Armonk, NY)
|
Family
ID: |
1000005174558 |
Appl.
No.: |
15/409,806 |
Filed: |
January 19, 2017 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20180204233 A1 |
Jul 19, 2018 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
30/0633 (20130101); G06Q 30/0206 (20130101); H04L
67/2833 (20130101) |
Current International
Class: |
G06Q
10/00 (20120101); G06Q 30/02 (20120101); G06Q
30/06 (20120101); H04L 29/08 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
https://stackoverflow.com/questions/34184200/finding-the-centered-average--
of-a-list (Year: 2015). cited by examiner .
https://stackoverflow.conn/questions/34184200/finding-the-centered-average-
-of-a-list (Year: 2015). cited by examiner .
SleBluue (Dec. 2015). Finding the centered average of a list.
https://stackoverflow.com/questions/34184200/finding-the-centered-average-
-of-a-list (Year: 2015). cited by examiner .
Chen, L., Mislove, A., & Wilson, C. (Apr. 2016). An empirical
analysis of algorithmic pricing on amazon marketplace. In
Proceedings of the 25th International Conference on World Wide Web
(pp. 1339-1349) (Year: 2016). cited by examiner .
Roberts, "Application of a Gaussian, Missing-Data Model to Product
Recommendation", IEEE Signal Processing Letters, vol. 17 (5), 2010,
pp. 509-512. cited by applicant .
Mell et al., "The NIST Definition of Cloud Computing", NIST,
Special Publication 800-145, Sep. 2011, 7 pages. cited by
applicant.
|
Primary Examiner: Waesco; Joseph M
Assistant Examiner: Stivaletti; Matheus
Attorney, Agent or Firm: Carusillo; Stephanie Wright; Andrew
D. Roberts, Calderon, Safran & Cole, P.C.
Claims
What is claimed is:
1. A computer-implemented method comprising: receiving, by a
computing device via computer network, a plurality of session data
records indicating computer network browsing activity between a
plurality of client devices and a merchant server hosting an online
store, the plurality of session data records being recorded based
on occurrences of shopping-related events including accessing a
webpage describing a single product of a plurality of products,
adding the single product to an online shopping cart, and
purchasing the single product from the online shopping cart, and
wherein each of the plurality of session data records corresponds
to a particular shopping-related event and identifies a price of
the single product, a shipping speed of the single product, and a
customer feedback rating of the particular product at a time of the
particular shopping-related event; aggregating, by the computing
device, a subset of the plurality of session data records for the
single product, of the plurality of products, identified in the
session data records and offered for purchase by the online store,
the aggregated subset of session data records identifying demand
activity for the single product by time period, including views of
the single product, cart additions of the single product, and
purchases of the single product, and further identifying an average
price of the single product, an average shipping speed of the
single product, and an average customer feedback rating of the
single product within a particular time period when the single
product was viewed, within a particular time period when the single
product was added to the online shopping cart, and within a
particular time period when the single product was purchased from
the online shopping cart; preprocessing, by the computing device,
the aggregated subset of session data records by interpolating
missing data for at least one time period; extracting, by the
computing device, features from the aggregated subset of session
data records relating to customer demand for the single product,
the features including at least one from the group consisting of a
centered average user rating and a log value of a quantity of items
purchased; modeling, by the computing device, customer demand for
the single product based on the extracted features and using a
regression of sales versus price; determining, by the computing
device, an optimized price for the single product based on results
of the modeling and rules provided by an administrator; and
publishing, by the computing device, the optimized price on the
webpage describing the single product.
2. The method of claim 1, wherein the extracting the features
includes: transforming a single data column; combining multiple
columns into a single feature; and representing a single column as
one-hot encoded columns.
3. The method of claim 1, wherein the modeling is further based on
model specifications provided by an administrator, wherein the
model specifications comprise at least one selected from a group
consisting of: a type of model to generate; a type of modeling
algorithms to use for generating the model; and types of features
to consider, disregard, or weigh more heavily when modeling the
customer demand.
4. The method of claim 1, wherein the rules provided by the
administrator comprise at least one business rule selected from a
group consisting of: sales; revenue; and profit.
5. The method of claim 1, wherein the preprocessing the aggregated
subset of session data records comprises populating data for which
aggregate data does not exist based on the interpolating missing
data, wherein the extracting is based on preprocessing the
aggregated session data records.
6. The method of claim 1, wherein each of the plurality of session
level data records further includes data comprising: a session
identifier; a product identifier of the single product; date and
time of a shopping-related event; and a quantity of the single
product.
7. The method of claim 6, wherein the aggregated subset of session
data records further includes average quantities for the single
product across the plurality of session data records.
8. The method of claim 1, further comprising updating the model for
customer demand based on receiving updated session data records as
browsing activity between the plurality of client devices and the
merchant server continues.
9. The method of claim 8, further comprising: repeating, by the
computing device, the determining the optimized price for the
single product based on results of the updating the model such that
the optimized price is adjusted based on short term changes in
demand; and repeating, by the computing device, the publishing the
optimized price on the webpage describing the single product.
10. The method of claim 9, further comprising: modifying, by the
computing device, a marketing campaign for the single product to
identify the optimized price; and adjusting, by the computing
device, a projected number of sales of the single product in a
particular time period based on the optimized price, and wherein
the rules provided by the administrator comprise business rules
based on sales, revenue, and profit.
11. The method of claim 1, wherein a service provider at least one
of creates, maintains, deploys and supports the computing
device.
12. The method of claim 1, wherein steps of claim 1 are provided by
a service provider on a subscription, advertising, and/or fee
basis.
13. The method of claim 1, wherein the computing device includes
software provided as a service in a cloud environment.
14. The method of claim 1, further comprising deploying a system
for modeling customer demand based on the plurality of session data
records and the determining the optimized price, comprising
providing a computer infrastructure operable to perform the steps
of claim 1.
15. A computer program product for modeling customer demand based
on a plurality of session data records and optimizing price, the
computer program product comprising a non-transitory computer
readable storage medium having program instructions embodied
therewith, the program instructions executable by a computing
device to cause the computing device to: monitor computer network
browsing activity between a plurality of client devices and a
merchant server; generate the plurality of session data records
having details regarding the computer network browsing activity
between the plurality of client devices and the merchant server
hosting an online store, the plurality of session data records
being generated based on occurrences of shopping-related events
including accessing a webpage describing a single product of a
plurality of products, adding the single product to an online
shopping cart, and purchasing the single product from the online
shopping cart, and wherein each of the plurality of session data
records corresponds to a particular shopping-related event and
identifies a price of the single product, a shipping speed of the
single product, and a customer feedback rating of the particular
product at a time of the particular shopping-related event;
aggregate a subset of the plurality of session data records for the
single product, of the plurality of products, identified in the
session data records and offered for purchase by the online store,
the aggregated subset of session data records identifying demand
activity for the single product by time period, including views of
the single product, cart additions of the single product, and
purchases of the single product, and further identifying an average
price of the single product, an average shipping speed of the
single product, and an average customer feedback rating of the
single product within a particular time period when the single
product was viewed, within a particular time period when the single
product was added to the online shopping cart, and within a
particular time period when the single product was purchased from
the online shopping cart; preprocess the aggregated subset of
session data records by interpolating missing data for at least one
time period; extract features from the aggregated subset of session
data records relating to customer demand for the single product,
the features including at least one from the group consisting of a
centered average user rating and a log value of a quantity of items
purchased; model customer demand for the single product based on
the extracted features and using a regression of sales versus
price; a determine an optimized price for the single product based
on results of the modeling and rules provided by an administrator;
and publish the optimized price on the webpage describing the
single product.
16. The computer program product of claim 15, wherein: the
preprocessing the aggregated subset of session data records
comprises populating data for which aggregate data does not exist
based on the interpolating missing data; and the extracting is
based on preprocessing the aggregated session data records.
17. The computer program product of claim 15, the program
instructions further cause the computing device to update the model
for customer demand based on receiving updated session data records
as browsing activity between the plurality of client devices and
the merchant server continues.
18. The computer program product of claim 15 wherein the plurality
of session level data records each include data selected from a
group consisting of: a session identifier; a product identifier of
the single product; date and time of a shopping-related event; and
a quantity of the single product.
19. A system comprising: a CPU, a computer readable memory and a
non-transitory computer readable storage medium associated with a
computing device; program instructions to gather session level data
from online customer retail activity, the session level data being
gathered based on occurrences of shopping-related events including
accessing a webpage describing a single product of a plurality of
products, adding the single product to an online shopping cart, and
purchasing the single product from the online shopping cart, and
wherein the session level data corresponds to shopping-related
events and identifies a price of the single product, a shipping
speed of the single product, and a customer feedback rating of the
particular product at a time of each of the shopping-related
events; program instructions to aggregate, for the single product,
the session level data pertaining to the single product, the
aggregated session level data identifying demand activity for the
single product by time period, including views of the single
product, cart additions of the single product, and purchases of the
single product, and further identifying an average price of the
single product, an average shipping speed of the single product,
and an average customer feedback rating of the single product
within a particular time period when the single product was viewed,
within a particular time period when the single product was added
to the online shopping cart, and within a particular time period
when the single product was purchased from the online shopping
cart; program instructions to substitute values by interpolation as
a replacement for missing data in the aggregated session level data
to generate a complete data set; program instructions to extract
one or more features of online customer behavior based on an
analysis of the complete data set, the features including at least
one from the group consisting of a centered average user rating and
a log value of a quantity of items purchased; program instructions
to determine a level of customer demand for the product based on
the one or more features of online customer behavior and using a
regression of sales versus price; and program instructions to
determine a modified price of the product on the webpage describing
the single product based on the level of customer demand and rules
provided by an administrator, wherein the program instructions are
stored on the computer readable storage medium for execution by the
CPU via the computer readable memory.
20. The system of claim 19, further comprising program instructions
to modify a marketing campaign for the product based on the level
of customer demand.
Description
BACKGROUND
The present invention generally relates to dynamic pricing of
products and, more particularly, to dynamic pricing based on online
retail behavioral data.
Traditional pricing systems are static and do not consider dynamic
changes in customer demand. Current dynamic pricing solutions
utilize rules and tests based systems for dynamic pricing. These
systems adjust product prices using predefined pricing rules or
test results based on behavior of small samples. The rules can be
based on competitor price changes, inventory levels, sales
objectives, and anticipated changes in demand. Purely rule based
pricing systems require analysts to manually set pricing rules for
each product or group of products. Revenue optimization depends on
the quality of the specified rules.
SUMMARY
In an aspect of the invention, a computer-implemented method
includes: receiving, by a computing device via computer network, a
plurality of session data records indicating computer network
browsing activity between a plurality of client devices and a
merchant server hosting an online store; aggregating, by the
computing device, a subset of the plurality of session data records
for a single product, of a plurality of products, identified in the
session data records and offered for purchase by the online store;
extracting, by the computing device, features from the aggregated
subset of session data records relating to customer demand for the
single product; modeling, by the computing device, customer demand
for the single product based on the extracted features; optimizing,
by the computing device, a price for the single product based on
results of the modeling; and publishing, by the computing device,
the optimized price.
In an aspect of the invention, there is a computer program product
for modeling customer demand based on a plurality of session data
records and optimizing price. The computer program product includes
a computer readable storage medium having program instructions
embodied therewith, the program instructions executable by a
computing device to cause the computing device to: monitor computer
network browsing activity between a plurality of client devices and
a merchant server; generate a plurality of session data records
having details regarding the computer network browsing activity
between the plurality of client devices and a merchant server
hosting an online store; aggregate a subset of the plurality of
session data records for a single product, of a plurality of
products, identified in the session data records and offered for
purchase by the online store; extract features from the aggregated
subset of session data records relating to customer demand for the
single product; model customer demand for the single product based
on the extracted features; optimize a price for the single product
based on results of the modeling; and publish the optimized
price.
In an aspect of the invention, a system includes: a CPU, a computer
readable memory and a computer readable storage medium associated
with a computing device; program instructions to gather session
level data from online customer retail activity; program
instructions to aggregate, for a product, the session level data
pertaining to the product; program instructions to substitute
values as a replacement for missing data in the aggregated session
level data to generate a complete data set; program instructions to
extract one or more features of online customer behavior based on
an analysis of the complete data set; program instructions to
determine a level of customer demand for the product based on the
one or more features of online customer behavior; and program
instructions to modify a price of the product based on the level of
customer demand. The program instructions are stored on the
computer readable storage medium for execution by the CPU via the
computer readable memory.
BRIEF DESCRIPTION OF THE DRAWINGS
The present invention is described in the detailed description
which follows, in reference to the noted plurality of drawings by
way of non-limiting examples of exemplary embodiments of the
present invention.
FIG. 1 depicts a cloud computing node according to an embodiment of
the present invention.
FIG. 2 depicts a cloud computing environment according to an
embodiment of the present invention.
FIG. 3 depicts abstraction model layers according to an embodiment
of the present invention.
FIG. 4 shows an overview of an example implementation in accordance
with aspects of the present invention
FIG. 5 shows an example environment in accordance with aspects of
the present invention.
FIG. 6 shows an example data structure representing session level
data records for product cart additions in accordance with aspects
of the present invention.
FIG. 7 shows example data structures representing aggregated
session level data based on the data structure of FIG. 6 in
accordance with aspects of the present invention.
FIG. 8 shows an example data structure representing preprocessed
aggregated data for a particular product and event type in
accordance with aspects of the present invention.
FIG. 9 shows an example data structure representing extracted
features data for a particular product and event type in accordance
with aspects of the present invention.
FIG. 10 shows a block diagram of example components of a demand
modeling and pricing server in accordance with aspects of the
present invention.
FIG. 11 shows an example flowchart for generating a customer demand
model for a product based on session data, and optimizing price for
a product based on the results of the customer demand model in
accordance with aspects of the present invention.
DETAILED DESCRIPTION
The present invention generally relates to dynamic pricing of
products and, more particularly, to dynamic pricing based on online
retail behavioral data. Aspects of the present invention may
include systems and/or methods that may model and/or predict
changes in customer demand based on online behavioral data
collected by online retailers. Further, aspects of the present
invention may include a dynamic pricing system that optimizes
product prices based on the predicted customer demand changes for
revenue maximization.
As described herein, a demand modeling and pricing system may
gather session level data from online merchants. As described
herein, an online merchant may record session level data
identifying attributes of the user's browsing activity that relate
to product demand. Specifically, the online merchant may store a
record based on the occurrence of an event (e.g., each time a user
accesses a webpage describing a product for sale, each time a user
adds a product to the user's online shopping cart, and/or each time
a user purchases a product from the user's shopping car). Each
record may identify data relating to product demand. For example, a
record may include a product identifier to identify the product
(e.g., the product viewed, added to a shopping cart, or bought from
a shopping cart). Additionally, or alternatively, the record may
include a date and time for the event (e.g., date and time for when
the product was viewed, added to the shopping cart, or bought from
the shopping cart). Additionally, or alternatively, the record may
identify a price of the product at the time of the event, a
shipping speed (e.g., express shipping, standard shipping, economy
shipping, etc.), a customer feedback rating, a quantity, etc.).
From the session level data, a demand modeling and pricing system
may extract features or attributes from the session level data that
relate to customer demand for a particular product. These features
are used to model and predict customer demand for the product as a
function of price. An optimal price for the product may be
determined based on the predicted demand. The process of
determining optimal price may be repeated for individual products.
In this way, prices may be adjusted based on short term changes in
demand. Further, the demand modeling and pricing system may
self-adjust to market response to price changes, may set prices to
optimize custom criteria for revenue, sales, or profit, and may
modify the demand model to suite available data. In embodiments,
session level data may be continuously monitored and recorded as
browsing activity between client devices and an online retailer
continues. In this way, customer demand modeling is updated
periodically, and accordingly, prices are continuously optimized
and published.
Aspects of the present invention are based on data records from an
online merchant or e-commerce environment in which customer demand
is represented by browsing data included in session level data.
Session level data may include hundreds, thousands, or tens of
thousands of data records of user web browsing activity in a
relatively short amount of time. Accordingly, it is emphasized that
the aspects of the present invention require the use of computing
devices and computer networks. Also, aspects of the present
invention transform raw data records (e.g., session level data)
into a usable form (e.g., demand models and pricing information)
which in turn is used for a specific and useful application of
improving revenue. As is described herein, aspects of the present
invention discuss non-conventional and non-routine steps of
receiving session data from an online merchant, aggregating the
session data for a product, preprocessing the aggregated session
data, extracting features from the preprocessed aggregated data,
modeling customer demand based on extracted features and model
specifications, and optimizing price based on model results and
business rules.
The present invention may be a system, a method, and/or a computer
program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
It is understood in advance that although this disclosure includes
a detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported providing transparency
for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 1, a schematic of an example of a cloud
computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
In cloud computing node 10 there is a computer system/server 12,
which is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context
of computer system executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
As shown in FIG. 1, computer system/server 12 in cloud computing
node 10 is shown in the form of a general-purpose computing device.
The components of computer system/server 12 may include, but are
not limited to, one or more processors or processing units 16, a
system memory 28, and a bus 18 that couples various system
components including system memory 28 to processor 16.
Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
Computer system/server 12 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system/server 12, and it includes both
volatile and non-volatile media, removable and non-removable
media.
System memory 28 can include computer system readable media in the
form of volatile memory, such as random access memory (RAM) 30
and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
nonremovable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
memory 28 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
Program/utility 40, having a set (at least one) of program modules
42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more
external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
Referring now to FIG. 2, illustrative cloud computing environment
50 is depicted. As shown, cloud computing environment 50 comprises
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or automobile computer system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 2 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
Referring now to FIG. 3, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 2) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 3 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions
described below. Resource provisioning 81 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing 82 provide cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may comprise application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal 83
provides access to the cloud computing environment for consumers
and system administrators. Service level management 84 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and demand
modeling and pricing 96.
Referring back to FIG. 1, the program/utility 40 may include one or
more program modules 42 that generally carry out the functions
and/or methodologies of embodiments of the invention as described
herein (e.g., such as the functionality provided by demand modeling
and pricing 96). Specifically, the program modules 42 may receive
session data from an online merchant, aggregate the session data
for a product, preprocesses the aggregated session data, extract
features from the preprocessed aggregated data, model customer
demand based on extracted features and model specifications, and
optimize price based on model results and business rules. Other
functionalities of the program modules 42 are described further
herein such that the program modules 42 are not limited to the
functions described above. Moreover, it is noted that some of the
modules 42 can be implemented within the infrastructure shown in
FIGS. 1-3. For example, the modules 42 may be representative of a
demand modeling and pricing server 230 shown in FIG. 4.
FIG. 4 shows an overview of an example implementation in accordance
with aspects of the present invention. As shown in FIG. 4, client
devices 210 may communicate with a merchant server 220 as users of
the client devices 210 browse a web page or online store hosted by
the merchant server 220 (step 1.1). The merchant server 220 may
record session data relating to the browsing activity and
communications between the merchant server 220 and the client
device 210 (step 1.2). For example, as described herein, the
merchant server 220 may generate a session level record identifying
attributes of the user's browsing activity that relate to product
demand. Specifically, the merchant server 220 may store a record
based on the occurrence of an event (e.g., each time a user
accesses a webpage describing a product for sale, each time a user
adds a product to the user's online shopping cart, and/or each time
a user purchases a product from the user's shopping car). Each
record may identify data relating to product demand. For example, a
record may include a product identifier to identify the product
(e.g., the product viewed, added to a shopping cart, or bought from
a shopping cart). Additionally, or alternatively, the record may
include a date and time for the event (e.g., date and time for when
the product was viewed, added to the shopping cart, or bought from
the shopping cart). Additionally, or alternatively, the record may
identify a price of the product at the time of the event, a
shipping speed (e.g., express shipping, standard shipping, economy
shipping, etc.), a customer feedback rating, a quantity, etc.).
At step 1.3, the merchant server 220 may provide the session data
(e.g., the session level records) to the demand modeling and
pricing server 230. At step 1.4, the demand modeling and pricing
server 230 may model the demand of individual products offered via
the merchant server 220 based on the session data. As an example,
the demand modeling and pricing server 230 may aggregate session
data for a particular product. The aggregated data may identify
product demand activity at different time periods (e.g., product
views, product cart additions, product purchases, etc.). For
example, the aggregated data may identify an average price, average
shipping speed, and/or average customer rating within a particular
time period when the product was subject to an event (e.g., when
the product was viewed, added to customer carts, purchased from
carts, etc.).
As described in greater detail with respect to FIG. 7, aggregated
data may be collected into a data structure in which each row of
the data structure identifies aggregated data for a time period.
The demand modeling and pricing server 230 may preprocess the
aggregated session data by interpolating any data that may be
missing within certain time periods (e.g., in a case in which
products were viewed, but not purchased). For example, as described
in greater detail with respect to FIG. 8, session level data may
not exist for certain time periods, and accordingly, the demand
modeling and pricing server 230 may populate empty fields in the
data structure to complete the data set. The demand modeling and
pricing server 230 may then extract features relating to product
demand from the completed data set. For example, the extracted
features may include a centered average user rating, a log value of
a quantity of items purchased, etc. Demand may be modeled based on
the extracted features and/or custom model specifications. Further,
price may be optimized based on the model results and/or business
rules (e.g., rules identifying price points based on model
results).
With continued reference to FIG. 4, at step 1.5, the demand
modeling and pricing server 230 may provide information regarding
pricing updates to the merchant server 220, and at step 1.6, the
merchant server 220 may update the product's price accordingly. In
this way, the demand modeling and pricing server 230 may
dynamically update product pricing based on session level data that
indicates customer demand. As a result, revenue may be improved for
online retailers.
FIG. 5 shows an example environment in accordance with aspects of
the present invention. As shown in FIG. 5, environment 500 may
include client device(s) 210, merchant server 220, demand modeling
and pricing server 230, and network 240. In embodiments, one or
more components in environment 500 may correspond to one or more
components in the cloud computing environment of FIG. 2. In
embodiments, one or more components in environment 200 may include
the components of computer system/server 12 of FIG. 1.
The client device 210 may include a device capable of communicating
via a network, such as the network 240. For example, the client
device 210 may correspond to a mobile communication device (e.g., a
smart phone or a personal digital assistant (PDA)), a portable
computer device (e.g., a laptop or a tablet computer), a desktop
computing device, and/or another type of computing device. In some
embodiments, the client device 210 may be used by a user to access
an online store hosted by the merchant server 220.
The merchant server 220 may include one or more computing/server
devices, such as the computer system/server device 12 of FIG. 1,
that hosts an online store accessible via a computer network, such
as the network 240. As described herein, the merchant server 220
may generate session level data records based on browsing
communications between the merchant server 220 and the client
device 210. The session level data records may include information
relating customer demand levels/patterns for a product at different
times.
The demand modeling and pricing server 230 may include one or more
computing/server devices that models customer demand based on
session level data gathered by the merchant server 220. Further,
the demand modeling and pricing server 230 may determine pricing
updates based on the results of the model, as described in greater
detail herein.
The network 240 may include network nodes, such as network nodes 10
of FIG. 2. Additionally, or alternatively, the network 240 may
include one or more wired and/or wireless networks. For example,
the network 240 may include a cellular network (e.g., a second
generation (2G) network, a third generation (3G) network, a fourth
generation (4G) network, a fifth generation (5G) network, a
long-term evolution (LTE) network, a global system for mobile (GSM)
network, a code division multiple access (CDMA) network, an
evolution-data optimized (EVDO) network, or the like), a public
land mobile network (PLMN), and/or another network. Additionally,
or alternatively, the network 235 may include a local area network
(LAN), a wide area network (WAN), a metropolitan network (MAN), the
Public Switched Telephone Network (PSTN), an ad hoc network, a
managed Internet Protocol (IP) network, a virtual private network
(VPN), an intranet, the Internet, a fiber optic-based network,
and/or a combination of these or other types of networks.
The quantity of devices and/or networks in the environment 500 is
not limited to what is shown in FIG. 5. In practice, the
environment 500 may include additional devices and/or networks;
fewer devices and/or networks; different devices and/or networks;
or differently arranged devices and/or networks than illustrated in
FIG. 5. Also, in some implementations, one or more of the devices
of the environment 500 may perform one or more functions described
as being performed by another one or more of the devices of the
environment 500. Devices of the environment 500 may interconnect
via wired connections, wireless connections, or a combination of
wired and wireless connections.
FIG. 6 shows an example data structure 600 representing session
level data records for product cart additions. As shown in FIG. 6,
data structure 600 may include a header identifying an event type
(e.g., "cart additions" in which products are added to users'
carts). Each row below the header represents a data record from a
session between a client device 210 and merchant server 220. Each
row may store attributes of the session, such as a session ID, a
product ID for a product, a data and time corresponding to the
event (e.g., data and time for when a corresponding product was
added to a cart), a price of the product at the time of the event,
a selected shipping speed corresponding to the event, an average
customer rating at the time of the event, and a product quantity
(e.g., a quantity of items added to the cart). As a specific
example, data structure 600 may store a session level data record
with session ID of S12 showing that a quantity of one of product
having the product ID of P01 was added to a cart on Jan. 24, 2014.
At the time the product was added to the cart, the price of the
product was 24, the average rating of the product was 4, and
shipping speed was one day.
The session level data stored by data structure 600 may be recorded
by the merchant server 220 as client devices 210 communicate with
the merchant server 220 (e.g., as users browse product web pages
hosted by the merchant server 220). The session level data stored
by the data structure 600 may be provided to the demand modeling
and pricing server 230 (e.g., in order for the demand modeling and
pricing server 230 to aggregate, process, and/or model the session
level data). Also, session level data may be stored for other event
types (e.g., cart item purchases, product views, etc.).
FIG. 7 shows example data structures 700 and 750 representing
aggregated session level data based on data structure 600 of FIG.
6. As shown in FIG. 7, data structure 700 may include a header
identifying a single date and an event type. In the example shown,
data structure 700 represents aggregated session level data for the
date "Jan. 1, 2014" for the event type "cart item additions." Each
row in data structure 700 represents an aggregation of the session
level data records from data structure 600 for the date identified
in the header. Each row also represents an aggregation of the
session level data records at each time period. For example, a day
may be divided into two time periods (e.g., one time period from
00:00:00 to 11:59:59 and another time period from 12:00:00 to
23:59:59). In embodiments, the information stored by the data
structures 700 and 750 may be based on data of various other forms
other information other than online session data.
Referring back to data structure 600 in FIG. 6, two data records
were stored for product ID P01 within time period two on Jan. 1,
2014. Accordingly, data structure 700 stores a row of data
identifying the average base price for these two data records
(e.g., an average price of 24 since both records had a price of
24), a number of records with one-day shipping speed (e.g., one
record), a number of records with two day shipping speed (one
record), an average customer rating (4.5 being the average of a
record with a rating of 4 and the other record with a rating of 5),
and an average quantity (1.5 being the average of a record with a
quantity of 2 and the other record with a quantity of 1).
Similarly, data structure 700 stores a row of data identifying the
average base price, shipping speeds, average ratings, and
quantities for products P02 and P03 on Jan. 1, 2014. Since only one
record was stored for products, P02 and P03, the data in data
structure 700 matches that of data structure 600. In embodiments,
aggregate data for multiple dates may be generated such that a data
structure 700 is generated for other dates (e.g., for Jan. 2, 2014;
Jan. 3, 2014, etc.). Also a data structure 700 may be generated for
other event types (e.g., product views, cart purchases, etc.).
Data structure 750 stores aggregate data for a particular product
(e.g., product ID P01) for a particular event type (e.g., cart item
additions). Information stored by data structure 750 may be based
on the data stored by data structure 600 and 700. For example, an
entry or row in data structure 750 stores date, time period,
average base price, shipping speed counts, average rating, and
average quantity. As an example, data structure 750 may store
aggregated data for product P01 for time period two on date Jan. 1,
2014. The aggregated data for this row of data may correspond to
the data from the row in data structure 700 associated with product
P01 and the date of Jan. 1, 2014.
A subsequent row in data structure 750 may store aggregated data
for product P01 for the time period one on date Jan. 3, 2014. The
aggregate data for this row of data may correspond to data for
product P01 for the date Jan. 3, 2014 (as would be stored in a
different data structure 700 associated with the data Jan. 3,
2014). Also, the aggregate data for this row of data may be derived
from data structure 600. For example, referring back to data
structure 600, session IDs S78 and S89 represent session data
records for product P01 during time period one on Jan. 3, 2014.
Accordingly, data structure 750 stores an entry with the aggregated
data for session IDs S78 and S89. For example, data structure 750
stores an entry showing a time period of one, an average base price
of 23.5 (e.g., corresponding to the average price of 23 and 24 from
records with session IDs S78 and S89 from data structure 600), one
one-day shipping speed count (e.g., from session data record S78),
one three day shipping speed count (e.g., from session data record
S89), an average customer rating of 4.5 (from a customer rating of
four from session data record S78 and a customer rating of five
from session data record S89), and a quantity of 2 (a quantity of
three from session data record S78 and a quantity of one from
session data record S89). Data structure 750 may also store an
entry for Jan. 3, 2014, time period two. Since data structure 600
only stores one entry for Jan. 3, 2014, time period two for product
P01, the entry in data structure 750 may match that of data
structure 600.
FIG. 8 shows an example data structure 800 representing
preprocessed aggregated data for a particular product and event
type. Similar to data structure 750, data structure 800 may include
a header that identifies product ID P01 and an event type of cart
item additions. The data from data structure 800 is based on the
data from data structure 750. As described herein, data stored in
data structure 800 includes interpolated data for data that was not
present in data structure 750. For example, referring to data
structure 750, a row does not exist for the date of Jan. 1, 2014,
time period one. Further, a row does not exist for Jan. 2, 2014,
time period one, or Jan. 2, 2014, time period two. Accordingly, in
data structure 800, the "missing" data is populated with data
(e.g., substitute values) to reflect data from other rows in which
data exists (e.g., as shown by the shaded rows in data structure
800). In embodiments, data may be missing in a situation where a
product was viewed, but not added to the cart. Missing rows may
also be noted as "zero sales" and information regarding zero sales
may be used to model customer demand.
FIG. 9 shows an example data structure 900 representing extracted
features data for a particular product and event type. Similar to
data structure 750 and data structure 800, data structure 900 may
include a header that identifies product ID P01 and an event type
of cart item additions. The data from data structure 900 is based
on the data from data structure 750 and/or data structure 800. In
embodiments, data structure 900 may extract features, which may
include, for example, data of data structure 800 with statistical
operations applied. For example, data structure 900 may include a
"centered average rating" and a Log value of the aggregate quantity
data from data structure 800. Additionally, or alternatively,
feature extraction may include transforming a single data column,
combining multiple columns into a single feature, and/or
representing a single column in the form of one-hot encoded
columns. From the extracted features data represented by data
structure 900, customer demand may be modeled. For example, a
log-linear regression of sales versus price and additional features
may be used to model product level customer demand.
FIG. 10 shows a block diagram of example components of a demand
modeling and pricing server in accordance with aspects of the
present invention. As shown in FIG. 10, the demand modeling and
pricing server 230 may include a session data recording module
1010, a session data aggregation module 1020, a aggregated data
preprocessing module 1030, a feature extraction module 1040, a
customer demand modeling module 1050, and a price optimization
module 1060. In embodiments, the demand modeling and pricing server
230 may include additional or fewer components than those shown in
FIG. 10. In embodiments, separate components may be integrated into
a single computing component or module. Additionally, or
alternatively, a single component may be implemented as multiple
computing components or modules.
The session data recording module 1010 may include a program module
(e.g., program module 42 of FIG. 1) that records session data
records based on communications between the client devices 210 and
the merchant server 220. Each data record may be recorded based on
the occurrence of a shopping-related event (e.g., each time a
client device 210 access a product description page, adds an item
to a card, purchases an item, etc.). Each data record may identify
a session ID, a product ID, a date/time (corresponding to a date
and time period designation), a base price, a shipping speed, an
average customer rating, a quantity, etc. Examples of session data
records are described above with respect to data structure 600 in
FIG. 6.
The session data aggregation module 1020 may include a program
module (e.g., program module 42 of FIG. 1) that aggregates session
data for multiple products corresponding to a particular event type
and day. In embodiments, the session data aggregation module 1020
may aggregate the session data by product ID, date, and time
period, such that each aggregated record identifies an average
price, shipping speed counts, average customer rating, and average
quantity. Examples of aggregated session data are described above
with respect to data structure 700 and data structure 750 in FIG.
7.
The aggregated data preprocessing module 1030 may include a program
module (e.g., program module 42 of FIG. 1) that preprocesses the
aggregated data generated by the session data aggregation module
1020. For example, the aggregated data preprocessing module 1030
may preprocess the aggregated data by "filling in the blanks" or
populating data for which aggregated data does not exist (with
substitute values). Examples and further descriptions of
preprocessing the aggregated data are described above with respect
to data structure 800 in FIG. 8.
The feature extraction module 1040 may include a program module
(e.g., program module 42 of FIG. 1) that extracts features from the
preprocessed aggregated data. For example, the feature extraction
module 1040 may apply statistical operations to the preprocessed
aggregated data to better represent customer demand. Additionally,
or alternatively, feature extraction may include transforming a
single data column, combining multiple columns into a single
feature, and/or representing a single column in the form of one-hot
encoded columns. Examples and further descriptions regarding
extracted features are described above with respect to data
structure 900 in FIG. 9.
The customer demand modeling module 1050 may include a program
module (e.g., program module 42 of FIG. 1) that models customer
demand using, for example, the extracted feature data generated by
the feature extraction module 1040. For example, a log-linear
regression of sales versus price and additional features may be
used to model product level customer demand. Additionally, or
alternatively, other types of models may be used based on the
extracted feature data. In embodiments, the customer demand
modeling module 1050 may model the customer demand based on model
specifications provided by a user or analyst. For example, the
customer demand modeling module 1050 may receive model
specifications, such as the type of model to generate, the type of
modeling algorithms to use, and the types of features to consider,
disregard, or weigh more heavily when generating the model.
The price optimization module 1060 may include a program module
(e.g., program module 42 of FIG. 1) that determines an optimized
price for a product based on the results of the customer demand
model generated by the customer demand modeling module 1050. As
described herein, an optimized price may refer to a price that
maximizes a business value, such as profit, revenue, quantity of
sales, or the like. In embodiments, the price optimization module
1060 may determine an optimized price for a product further based
on business rules received from an administrator. For example, the
optimized price may be determined based on business rules or
business objective criterion such as sales, revenue, profit,
etc.
As an example, an optimized price may be determined based on a
model that may be derived from the information from data structure
900 of FIG. 9, as follows:
log(qty+1)=.alpha..sub.1*shipping1day+.alpha..sub.2*shipping2day-
+.alpha..sub.3*shipping3day+.alpha..sub.4*rating+.beta.*price=C+.beta.*pri-
ce (1) where .alpha. and .beta. are the coefficients estimated and
C=.alpha..sub.1*shipping1day+a.sub.2*shipping2day+.alpha..sub.3*shipping3-
day+.gamma.*rating
Equation (1) simplifies to: qty=exp(C+.beta.*price)-1 (2)
Equation (2) may be optimized to maximize revenue and profit. For
example, maximum revenue may be determined as:
revenue=qty*price=exp(C+.beta.*price)*price (3)
For a given cost, maximized profit may be determined as:
profit=revenue-cost=(exp(C+.beta.*price)*price-cost) (4)
Optimization techniques may then be used to maximize revenue based
on equation (3) or to maximize profit based on equation (4). In
embodiments, a threshold can be determined as a maximum profit or
revenue based on the above model. Optimal price may be determined
as one that satisfies this threshold. As should be understood by
those of ordinary skill in the art, other techniques and variations
for determining an optimal price that are within the scope and
spirit of the above description may be used.
Information regarding the optimized price may be used to update the
price for the product in order to achieve the business objective
criterion. For example, the information regarding the optimized
price may be published (e.g., on a web page for the product). In
embodiments, the information regarding the optimized price may be
provided to the merchant server 220 so that the merchant server 220
may publish the optimized price.
FIG. 11 shows an example flowchart for generating a customer demand
model for a product based on session data, and optimizing price for
a product based on the results of the customer demand model. The
steps of FIG. 11 may be implemented in the environment of FIGS. 4
and 5, for example, and are described using reference numbers of
elements depicted in FIGS. 4 and 5. As noted above, the flowchart
illustrates the architecture, functionality, and operation of
possible implementations of systems, methods, and computer program
products according to various embodiments of the present
invention.
As shown in FIG. 11, process 1100 may include receiving or
recording session data (step 1110). For example, as described above
with respect to the session data recording module 1010, the demand
modeling and pricing server 230 may receive or record session data.
In embodiments, the demand modeling and pricing server 230 may
record session data records and provide the data records to the
demand modeling and pricing server 230. Additionally, or
alternatively, the demand modeling and pricing server 230 may
monitor session activity between client devices 210 and merchant
server 220 and may record the session data into records. Examples
of session data records are described above with respect to data
structure 600 in FIG. 6.
Process 1100 may further include aggregating the session data (step
1120). For example, as described above with respect to the session
data aggregation module 1020, the demand modeling and pricing
server 230 may aggregate session data for multiple products
corresponding to a particular each event type and day. In
embodiments, the demand modeling and pricing server 230 may
aggregate the session data by product ID, date, and time period,
such that each aggregated record identifies an average price,
shipping speed counts, average customer rating, and average
quantity. Examples of aggregated session data are described above
with respect to data structure 700 and data structure 750 in FIG.
7.
Process 1100 may also include preprocessing the aggregated session
data (step 1130). For example, as described above with respect to
the aggregated data preprocessing module 1030, the demand modeling
and pricing server 230 may preprocess the aggregated data generated
at step 1120. For example, the demand modeling and pricing server
230 may preprocess the aggregated data by "filling in the blanks"
or populating data (e.g., with substitute values) for which
aggregated data does not exist in order generate a complete data
set. Examples and further descriptions of preprocessing the
aggregated data are described above with respect to data structure
800 in FIG. 8.
Process 1100 may further include extracting features from the
preprocessed aggregated session data (step 1140). For example, as
described above with respect to the feature extraction module 1040,
the demand modeling and pricing server 230 may extract features
from the preprocessed aggregated session data. In embodiments, the
demand modeling and pricing server 230 may apply statistical
operations to the preprocessed aggregated data to better represent
customer demand. Additionally, or alternatively, feature extraction
may include transforming a single data column, combining multiple
columns into a single feature, and/or representing a single column
in the form of one-hot encoded columns. Examples and further
descriptions regarding extracted features are described above with
respect to data structure 900 in FIG. 9.
Process 1100 may also include modeling customer demand based on
extracted features and model specifications (step 1150). For
example, as described above with respect to the customer demand
modeling module 1050, the demand modeling and pricing server 230
may model customer demand using, for example, the extracted feature
data generated by the feature extraction module 1040. In
embodiments, the customer demand modeling module 1050 may model the
customer demand to determine a level of customer demand based on
model specifications provided by a user or analyst. For example,
the customer demand modeling module 1050 may receive model
specifications, such as the type of model to generate, the type of
modeling algorithms to use, and the types of features to consider
or disregard when generating the model.
Process 1100 may further include optimizing price based on model
results and business rules (step 1160). For example, as described
above with respect to the price optimization module 1060, the
demand modeling and pricing server 230 may determine an optimized
price for a product based on the results of the customer demand
model generated by at step 1150. In embodiments, the demand
modeling and pricing server 230 may determine an optimized price
for a product further based on business rules received from an
administrator. For example, the optimized price may be determined
based on business rules or business objective criterion such as
sales, revenue, profit, etc. Information regarding the optimized
price may be used to update or modify the price for the product in
order to achieve the business objective criterion. Additionally, or
alternatively, information regarding the optimized price may be
used to modify a marketing campaign for the product (e.g., devote
more resources to a improve visibility of a low-demand item).
Process 1100 may further include publishing the optimized price
(step 1170). For example, as described above with respect to the
price optimization module 1060, the demand modeling and pricing
server 230 may publish the optimized price (e.g., on a web page for
the product). In embodiments, the information regarding the
optimized price may be provided to the merchant server 220 so that
the merchant server 220 may publish the optimized price.
In embodiments, the merchant may use information regarding the
optimized price to deploy a marketing campaign that is based on the
optimized price. For example, the marketing campaign may identify
the optimized price (or a different price that may not necessarily
be identical to the optimized price). In embodiments, the optimized
price may further be used to adjust a business model, projections,
or the like (e.g., projected number of sales in a given time
period, or to adjust an input number of sales for a business
model).
In embodiments, process 1100 may be repeated as browsing activity
between the client devices 210 and the merchant server 220
continues and as more session data records are recorded/received.
In this way, customer demand modeling is updated periodically, and
accordingly, prices are continuously optimized and published.
In embodiments, a service provider, such as a Solution Integrator,
could offer to perform the processes described herein. In this
case, the service provider can create, maintain, deploy, support,
etc., the computer infrastructure that performs the process steps
of the invention for one or more customers. These customers may be,
for example, any business that uses technology. In return, the
service provider can receive payment from the customer(s) under a
subscription and/or fee agreement and/or the service provider can
receive payment from the sale of advertising content to one or more
third parties.
In still additional embodiments, the invention provides a
computer-implemented method, via a network. In this case, a
computer infrastructure, such as computer system/server 12 (FIG.
1), can be provided and one or more systems for performing the
processes of the invention can be obtained (e.g., created,
purchased, used, modified, etc.) and deployed to the computer
infrastructure. To this extent, the deployment of a system can
comprise one or more of: (1) installing program code on a computing
device, such as computer system/server 12 (as shown in FIG. 1),
from a computer-readable medium; (2) adding one or more computing
devices to the computer infrastructure; and (3) incorporating
and/or modifying one or more existing systems of the computer
infrastructure to enable the computer infrastructure to perform the
processes of the invention.
The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *
References